• Journal of Convergence for Information Technology
• /
• v.11 no.9
• /
• pp.138-144
• /
• 2021

In the electronic component assembly business, when product defects occur, it is important to track incoming raw material defects or work defects, and it is important to improve suppliers or work sites according to the results. The core task of the smart factory is to build an integrated data hub to process storage, management, and analysis in real time, and to manage cluster processes, energy, environment, and safety. In order to improve reliability through accurate analysis and collection of production data by real-time monitoring of production site management for electronic parts-related small and medium-sized enterprises (SMEs), the establishment of a smart factory is essential. This paper was developed to be utilized in the construction by defining the system configuration method, smart factory-related technology and application cases, considering the characteristics of SMEs related to electronic components that want to introduce a smart factory.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.1
• /
• pp.82-95
• /
• 2019

In this study, we analyzed the beam time series of ocean reverberation which was conducted in the eastsouthern region of East Sea, Korea during the August, 2015. The reverberation data was gathered by moving research vessel towing LFM (Linear Frequency Modulation) source and triplet receiver array. After signal processing, we analyzed the variation of ocean reverberation level according to the seafloor bathymetry, source/receiver depth and sound speed profile. In addition, we used the normalized data by using cell averaging algorithm and identified the statistical characteristics of seafloor scatterer by using moment estimation method and estimated shape parameter. Also, we analyzed the coincidence of data with Rayleigh and K-distribution probability by Kolmogorov-Smirnov test. The results show that there is range dependency of reverberation according to the bathymetry and also that the time delay and the intensity level change depend on the depths of source and receiver. In addition, we observed that statistical characteristics of similar Rayleigh probability distribution in the ocean reverberation.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.145-153
• /
• 2020

This study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and is based on artificial intelligence-based HPAI spread analysis and patterning. Highly Pathogenic Avian Influenza (HPAI) is coming from abroad through migratory birds, but it is not clear exactly how it spreads to farms. In addition, it is assumed that the main cause of the spread is the vehicle, but the main cause of the spread is not exactly known. However, it is necessary to analyze the relationship between the vehicles and the facilities at the farms where they occur, as the type of vehicles that visit the farms most frequently is between farms and facilities, such as livestock transportation and feed transportation. In this paper, based on the Korea Animal Health Integrated System (KAHIS) data provided by Animal and Plant Quarantine Agency, the main cause of HPAI virus transfer is to be confirmed between vehicles and facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.2
• /
• pp.17-32
• /
• 2022

This study explored an approach to estimate a flow-density diagram(FD) on a link in highway traffic environment by utilizing probe vehicles' time headway records. To study empirical flow-density diagram(EFD), the probe vehicles with vision sensors were recruited for collecting driving records for nine months and the vision sensor data pre-processing and GIS-based map matching were implemented. Then, we examined the new EFDs to evaluate validity with reference diagrams which is derived from loop detection traffic data. The probability distributions of time headway and distance headway as well as standard deviation of flow and density were utilized in examination. As a result, it turned out that the main factors for estimation errors are the limited number of probe vehicles and bias of flow status. We finally suggest a method to improve the accuracy of EFD model.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.476-487
• /
• 2022

International research cooperation is emerging as one of the strategies for improving research performance. Therefore, in this study, through the analysis of research reports on the theme of international research cooperation, the subject and issues of international research cooperation were identified and the characteristics of these studies were confirmed. To this end, related report data were constructed, statistical data analysis and big data analysis of the data were performed. Considering the current international research cooperation network, it is necessary to conduct international research cooperation centered on developing countries while paying attention to the increase in China's proportion of international research cooperation. Second, it is necessary to emphasize the importance of international research cooperation in various countries, including developing countries, in that the interdependence of research between countries increases and the citation index of actual joint research is higher. Third, it can be seen that the subject field in which international research cooperation can be activated may vary depending on the type of support project. Therefore, it suggests that in order for international research cooperation on more diverse topics to be carried out, projects supporting them must also be diversified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.95-102
• /
• 2022

This paper proposed a linear time algorithm for a three-partition problem(TPP) in which a polynomial time algorithm is not known as NP-complete. This paper proposes a backtracking method that improves the problems of not being able to obtain a solution of the MM method using the sum of max-min values and third numbers, which are known polynomial algorithms in the past. In addition, the problem of MM applying the backtracking method was improved. The proposed algorithm partition the descending ordered set S into three and assigned to the forward, backward, and best-fit allocation method with maximum margin, and found an optimal solution for 50.00%, which is 5 out of 10 data in initial allocation phase. The remaining five data also showed performance to find the optimal solution by exchanging numbers between surplus boxes and shortage boxes at least once and up to seven times. The proposed algorithm that performs simple allocation and exchange optimization with less O(k) linear time performance complexity than the three-partition m=n/3 data, and it was shown that there could be a polynomial time algorithm in which TPP is a P-problem, not NP-complete.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.3
• /
• pp.563-570
• /
• 2018

This paper proposes an efficient dissolved gas analysis(DGA) and classification method of an oil-filled transformer using machine learning algorithms to solve problems inherent in IEC 60599. In IEC 60599, a certain diagnosis criteria do not exist, and duplication area is existed. Thus, it is difficult to make a decision without any experts since the IEC 60599 standard can not support analysis and classification of gas date of a power transformer in that criteria. To address these issue. we propose a dissolved gas analysis(DGA) and classification method using a machine learning algorithm. We evaluate the performance of the proposed method using support vector machines with dissolved gas dataset extracted from a power transformer in the real industry. To validate the performance of the proposed method, we compares the proposed method with the IEC 60599 standard. Experimental results show that the proposed method outperforms the IEC 60599 in the classification accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.9
• /
• pp.2097-2104
• /
• 2013

Scientific and technological research data is increasing exponentially and, Kisti(Korea Institute of Science and Technology Information) built and supported GSDC(Global Science experimental Data hub Center) depending on the needs processing large data computing and storage devices. Mobile web standards-based the MUPS was built for global community and services of gsdc to spreading mobile devices rapidly. And for analyze Operational status and system resources of GSDC at n this paper researched and implemented system resource monitor method of gsdc to mobile web environment, in this paper researched and implemented system resource monitor method of gsdc to mobile web environment. Research support system of GSDC operated by scientific linux. Sysstat resource monitoring tools create a daily report through sar(system analysis report), after sadc(system activity data collector) collected system resource utilization information. In this paper, sar reports designed and implemented in mobile web to can visualize in a mobile environment. We do not depend specific OS by implementation of the mobile web. So we are available in variety mobile OS. And through provided visual graph, this system can monitor easily and more conveniently then the existing system.

• The Korean Journal of Archival Studies
• /
• no.34
• /
• pp.57-105
• /
• 2012

The CAMS(Central Archives Management System) of NAK(National Archives of Korea) is an important system which receives and manages large amount of electronic records annually from 2015. From the point of view in database design, this paper analyzes the database schema of CAMS and discusses the direction of overall improvement of the CAMS. Firstly this research analyzes the tables for records and folders in the CAMS database which are core tables for the electronic records management. As a result, researchers notice that it is difficult to trust the quality of the records in the CAMS, because two core tables are entirely not normalized and have many columns whose roles are unknown. Secondly, this study suggests directions of normalization for the tables for records and folders in the CAMS database like followings: First, redistributing the columns into proper tables to reduce the duplication. Second, separating the columns about the classification scheme into separate tables. Third, separating the columns about the records types and sorts into separate tables. Lastly, separating metadata information related to the acquisition, takeover and preservation into separate tables. Thirdly, this paper suggests considerations to design and manage the database schema in each phase of archival management. In the ingest phase, the system should be able to process large amount of records as batch jobs in time annually. In the preservation phase, the system should be able to keep the management histories in the CAMS as audit trails including the reclassification, revaluation, and preservation activities related to the records. In the access phase, the descriptive metadata sets for the access should be selected and confirmed in various ways. Lastly, this research also shows the prototype of conceptual database schema for the CAMS which fulfills the metadata standards for records.

• Korean Journal of Remote Sensing
• /
• v.38 no.3
• /
• pp.299-309
• /
• 2022

Recently, The sea water temperature around Korean Peninsula is steadily increasing. Water temperature changes not only affect the fishing ecosystem, but also are closely related to military operations in the sea. The purpose of this study is to suggest which model is more suitable for the field of water temperature prediction by attempting short-term water temperature prediction through various prediction models based on deep learning technology. The data used for prediction are water temperature data from the East Sea (Goseong, Yangyang, Gangneung, and Yeongdeok) from 2016 to 2020, which were observed through marine observation by the National Fisheries Research Institute. In addition, we use Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) techniques that show excellent performance in predicting time series data as models for prediction. While the previous study used only LSTM, in this study, the prediction accuracy of each technique and the performance time were compared by applying various techniques in addition to LSTM. As a result of the study, it was confirmed that Bidirectional LSTM and GRU techniques had the least error between actual and predicted values at all observation points based on 1 hour prediction, and GRU was the fastest in learning time. Through this, it was confirmed that a method using Bidirectional LSTM was required for water temperature prediction to improve accuracy while reducing prediction errors. In areas that require real-time prediction in addition to accuracy, such as anti-submarine operations, it is judged that the method of using the GRU technique will be more appropriate.